Prediction of clinicopathological features, multi-omics events and prognosis based on digital pathology and deep learning in HR+/HER2− breast cancer

作者：Jia Hu, Hong Lv, Shen Zhao, Cai‐Jin Lin, Guan-Hua Su, Zhi-Ming Shao · 发表于：Journal of Thoracic Disease · 年份：2023 · DOI：10.21037/jtd-23-445 · 被引用次数：14 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Breast Cancer Treatment Studies、AI in cancer detection

Background: Breast cancer has the highest incidence and mortality rates among women worldwide. Hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)− breast cancer is the most common molecular subtype, accounting for 50–79% of breast cancers. Deep learning has been widely used in cancer image analysis, especially for predicting targets related to precise treatment and patient prognosis. However, studies focusing on therapeutic target and prognosis predicting in HR+/HER2− breast cancer are lacking. Methods: This study retrospectively collected hematoxylin and eosin (H&E)-stained slides of HR+/HER2− breast cancer patients between January 2013 and December 2014 at Fudan University Shanghai Cancer Center (FUSCC) and scanned to generate whole-slide images (WSIs). Then, we built a deep-learning-based workflow to train and validate model to predict clinicopathological features, multi-omics molecular features and prognosis; the area under the curve (AUC) of the receiver operating characteristic (ROC) and the concordance index (C-index) of the test set were used to assess model effectiveness. Results: A total of 421 HR+/HER2− breast cancer patients were included in our study. Regarding clinicopathological features, grade III could be predicted with an AUC of 0.90 [95% confidence interval (CI): 0.84–0.97]. Regarding somatic mutations, TP53 and GATA3 mutation could be predicted with AUCs of 0.68 (95% CI: 0.56–0.81) and 0.68 (95% CI: 0.47–0.89), respectively. Regarding g...